# Cisco's Cybersecurity Insights Highlight AI's Economic Potential and Risks

> In his reflection on three decades of cybersecurity evolution, Greg Akers asserts that AI, while powerful, is just an extension of previous software challenges. He emphasizes the need for robust security frameworks to tackle the complexities of new technologies.

**Source**: blogs.cisco.com | **Published**: 2026-10-01 | **Type**: article

## Key Facts

- Cisco's 30-year history shows resilience in adapting to evolving cybersecurity threats.
- AI's rapid growth mirrors past tech trends, indicating potential for significant economic impact.
- Transparency in security practices can enhance trust, but risks exist if mismanaged.
- Aviation's safety model suggests a need for systemic approaches in AI security to build confidence.
- Regulatory oversight is crucial as AI evolves, akin to air traffic control in aviation safety.

## Summary

In a recent reflection on the evolution of cybersecurity, Greg Akers, a veteran of Cisco, articulated the challenges and opportunities presented by artificial intelligence (AI) in the context of internet security. Drawing on his three decades of experience, Akers emphasized the historical gap between technological innovation and security measures, a gap that has persisted since the inception of the commercial internet. This ongoing disparity highlights the need for robust security frameworks as new technologies emerge, particularly AI, which is increasingly viewed with both excitement and trepidation.

The article outlines how, during the early days of the internet, cybersecurity was not a priority for companies like Cisco, which were primarily focused on building infrastructure. Akers argues that this historical context is crucial for understanding the current landscape of AI. He posits that AI should not be seen as a fundamentally new threat but rather as an extension of existing software challenges. The industry has previously navigated security concerns associated with mainframes, client-server systems, and cloud computing, albeit imperfectly. The implication is clear: the lessons learned from these experiences can inform how businesses approach AI security today.

Akers draws parallels between the aviation industry and the tech sector, suggesting that a similar systemic approach to safety could be beneficial for AI. In aviation, safety protocols are rigorously engineered, with independent oversight and a culture of transparency regarding failures. Akers advocates for a comparable model in AI, where technology companies collaborate with regulators to establish industry-wide standards and monitoring systems. This would not only enhance security but also foster public trust in AI technologies.

The article also addresses the cultural dimensions of cybersecurity, emphasizing the importance of humility among security practitioners. Akers notes that the best security efforts often go unnoticed, and failures can expose vulnerabilities that threaten the entire ecosystem. He stresses the need for transparency in disclosing security issues while balancing this with the risk of providing adversaries with critical information. This nuanced approach is essential for fostering a culture of trust and collaboration within the industry.

Looking ahead, Akers expresses optimism about the future of AI and cybersecurity, underscoring the importance of vigilance. He recognizes that predicting specific risks associated with AI is inherently challenging, yet he believes that applying layered-system thinking—similar to that used in aviation—can help mitigate these risks. The role of regulators will be pivotal in this process, akin to air traffic control, ensuring that the broader landscape remains safe as new technologies are integrated.

As AI continues to evolve at an unprecedented pace, businesses must adopt intentional and strategic approaches to security. The insights from Akers suggest that companies should not only focus on immediate security measures but also invest in long-term systemic solutions that can adapt to future challenges. This proactive stance will be critical for maintaining competitive advantage and ensuring the safe deployment of AI technologies in various sectors. The call for a collaborative framework between technology companies and regulators signals a shift toward a more integrated approach to security, one that could redefine industry standards and practices in the years to come.

## Entities

- **Companies**: Cisco
- **Technologies**: AI, cybersecurity, mainframe, client-server, broadband, cloud
- **People**: Greg Akers
- **Organizations**: air traffic control

## Key Concepts

cybersecurity evolution, AI as software, safety in innovation, systemic design in safety, transparency in security, humility in security practices, role of regulators, optimism in technology

## Definitions

- **AI**: Artificial Intelligence, a type of software designed to perform tasks that typically require human intelligence.
- **cybersecurity**: The practice of protecting systems, networks, and programs from digital attacks.
- **systemic design**: An approach to safety that emphasizes the importance of designing systems that account for human and technological factors.
- **transparency**: The practice of openly sharing information about vulnerabilities and security practices to improve overall safety.
- **regulators**: Government entities responsible for overseeing and enforcing laws and regulations within an industry.

## Use Cases

- monitoring AI systems in operations
- investigating failures in technology
- applying lessons from aviation to AI safety
- disclosing vulnerabilities in products
- implementing independent monitoring of AI
- creating industry-wide safety rules

## Frequently Asked Questions

**What is the main concern regarding AI?**

The main concern is the unpredictability of AI systems and the potential risks they pose. As AI evolves, it can behave in ways that are not fully anticipated, which requires careful management.

**How can we ensure the safety of AI systems?**

Ensuring safety involves applying systemic design principles, similar to those used in aviation. This includes independent monitoring, transparent investigations, and industry-wide safety standards.

**What role does transparency play in cybersecurity?**

Transparency is crucial as it allows the industry to learn from vulnerabilities and improve security practices. However, it must be balanced to prevent giving adversaries a roadmap to exploit weaknesses.

**Why is humility important in security practices?**

Humility is important because it acknowledges that no system is infallible. Security practitioners must recognize their limitations and be open to learning from failures to enhance overall safety.

**What can we learn from the aviation industry?**

The aviation industry teaches us that safety must be engineered as a system, not reliant on individual expertise. This approach can be applied to AI to ensure robust safety measures are in place.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/ciscos-cybersecurity-insights-highlight-ais-economic-potential-and-risks)
- [Original source](https://blogs.cisco.com/news/30-years-on-the-frontlines-of-the-internet-why-im-optimistic-about-ai)
- [Cisco](https://welcome.ai/company/cisco): Featured company

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Source: Welcome.AI | https://welcome.ai/content/ciscos-cybersecurity-insights-highlight-ais-economic-potential-and-risks